1. Field of the Invention
This invention relates generally to a method for estimating battery parameters that uses multiple sampling rates and, more particularly, a method for estimating battery parameters that uses two different sampling times where a battery terminal voltage and current are sampled at a high sampling rate to estimate the battery open circuit voltage (OCV) and high frequency resistance and the battery terminal voltage and current is re-sampled at a low sampling rate to estimate battery parameters that can be used to estimate battery power.
2. Discussion of the Related Art
Electric vehicles are becoming more and more prevalent. These vehicles include hybrid vehicles, such as the extended range electric vehicles (EREV) that combines a battery and a main power source, such as an internal combustion engine, fuel cell systems, etc., and electric only vehicles, such as the battery electric vehicles (BEV). All of these types of electric vehicles employ a high voltage battery that includes a number of battery cells. These batteries can be different battery types, such as lithium ion, nickel metal hydride, lead acid, etc. A typical high voltage battery for an electric vehicle may include 196 battery cells providing about 400 volts of power. The battery can include individual battery modules where each battery module may include a certain number of battery cells, such as twelve cells. The individual battery cells may be electrically coupled in series, or a series of cells may be electrically coupled in parallel, where a number of cells in the module are connected in series and each module is electrically coupled to the other modules in parallel. Different vehicle designs include different battery designs that employ various trade-offs and advantages for a particular application.
Batteries play an important role in powering electrical vehicles and hybrid vehicles. The effectiveness of battery control and power management is essential to vehicle performance, fuel economy, battery life and passenger comfort. For battery control and power management, two states of the battery, namely, state of charge (SOC) and battery power, need to be predicted, or estimated, and monitored in real time because they are not measurable during vehicle operation. Battery state of charge and battery power can be estimated using a simple equivalent circuit model of the battery that defines the battery open circuit voltage (OCV), battery ohmic resistance and an RC pair including a resistance and a capacitance using the battery terminal voltage and current. Therefore, both battery states have to be derived from battery parameters estimated from the battery terminal voltage and current. A few battery state estimation algorithms have been developed in the art using different methodologies and some have been implemented in vehicles.
It is well known that battery dynamics are generally non-linear and highly dependent on battery operating conditions. However, for onboard battery parameter estimation, a linear model with a few frequency modes is used to approximate a battery's dominant dynamics for a specific application, such as power prediction or SOC estimation. The reason for this is mainly due to limited computational power and memory available for onboard applications. In fact, even if there were unlimited computational power and memory, the accurate estimation of all battery parameters in a complex model with as many frequency modes as possible cannot be guaranteed because the excitation of signals, normally battery terminal voltage and terminal current, is limited. Therefore, it is neither practical nor necessary to cover all frequency modes in one model as long as the estimation error caused by model uncertainties is within an acceptable range for a specific application.
In order to minimize the memory and computational cost, a battery model that is as simple as possible is highly preferred. On the other hand, different applications need to be characterized by different frequency modes. For instance, the feature frequency to characterize the high frequency resistance of the battery is much higher than the feature frequency that characterizes the change in battery power. A simple model with limited frequency modes inevitably introduces errors and uncertainties because it cannot fully cover all feature frequencies for various applications.
U.S. patent application Ser. No. 11/867,497, filed Oct. 4, 2007, now published as Publication No. U.S. 2009/0091299, titled Dynamically Adaptive Method For Determining The State of Charge of a Battery, assigned to the assignee of this invention and herein incorporated by reference, discloses a method for determining battery state of charge and battery power using four battery parameters, namely, the battery OCV, ohmic resistance, and the resistance and capacitance of an RC pair.
The currently existing battery state estimation algorithms are based on a single sampling rate. Without adding more dynamic components, i.e., additional frequency modes, into the battery model, these algorithms have difficulties capturing both fast dynamics for SOC estimation and slow dynamics for power prediction. Therefore, the accuracy and robustness of the algorithms are compromised for multiple applications. In other words, the sampling rate that measures the battery terminal voltage and current is generally too fast to accurately determine the parameters generated from the RC pair, which results in a somewhat inaccurate representation of the battery's power capabilities at any given point in time.